The TABL network [1]} treats the time-series as a second order tensor \(\mathbf {X} \in \mathbb {R}^{D \times T}\) , where \(T\)  is the number of time-instances in the time-series. The building block of the network is a bilinear mapping based on parameters \(\mathbf {W}_1 \in \mathbb {R}^{D^{\prime } \times D}\)  which are used for applying data transformation, \(\mathbf {W}_2 \in \mathbb {R}^{T \times T^{\prime }}\)  which are used for aggregating information at the time domain, and \(\mathbf {B} \in \mathbb {R}^{D^{\prime } \times T^{\prime }}\)  which are used as a bias term. Moreover, another parameter matrix \(\mathbf {W} \in \mathbb {R}^{T \times T}\)  is used to incorporate an attention mechanism in the bilinear projection. The processing steps applied in the TABL layer are illustrated in Figure REF , and are the following:
